Outcome and biomarker supervised deep learning for survival prediction in two multicenter breast cancer series

Background: Prediction of clinical outcomes for individual cancer patients is an important step in the disease diagnosis and subsequently guides the treatment and patient counseling. In this work, we develop and evaluate a joint outcome and biomarker supervised (estrogen receptor expression and ERBB...

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Autores principales:	Dmitrii Bychkov, Heikki Joensuu, Stig Nordling, Aleksei Tiulpin, Hakan Kücükel, Mikael Lundin, Harri Sihto, Jorma Isola, Tiina Lehtimäki, Pirkko-Liisa Kellokumpu-Lehtinen, Karl von Smitten, Johan Lundin, Nina Linder
Formato:	Artigo
Lenguaje:	Inglês
Publicado:	Elsevier 2022-01-01
Colección:	Journal of Pathology Informatics
Materias:	Breast cancer Convolutional neural networks Digital pathology ERBB2 gene Estrogen receptor Multitask deep learning
Acceso en línea:	http://www.sciencedirect.com/science/article/pii/S2153353922007659
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